code: def _preprocess_data(self):
      train_df = []
      for file in self.train_data:
          if isinstance(file, pd.DataFrame):
              train_df.append(file)
          else:
              train_df.append(pd.read_csv(file))
      if len(train_df) > 1:
          train_df = pd.concat(train_df)
      else:
          train_df = train_df[0]
  
      valid_df = None
      if len(self.valid_data) > 0:
          valid_df = []
          for file in self.valid_data:
              if isinstance(file, pd.DataFrame):
                  valid_df.append(file)
              else:
                  valid_df.append(pd.read_csv(file))
          if len(valid_df) > 1:
              valid_df = pd.concat(valid_df)
          else:
              valid_df = valid_df[0]
      return train_df, valid_df
  
imports: import io
  import os
  import uuid
  import zipfile
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional
  import pandas
  from autotrain import logger
  from autotrain.preprocessor.dreambooth import DreamboothPreprocessor
  from autotrain.preprocessor.tabular import TabularBinaryClassificationPreprocessor, TabularMultiClassClassificationPreprocessor, TabularMultiColumnRegressionPreprocessor, TabularMultiLabelClassificationPreprocessor, TabularSingleColumnRegressionPreprocessor
  from autotrain.preprocessor.text import LLMPreprocessor, Seq2SeqPreprocessor, TextBinaryClassificationPreprocessor, TextMultiClassClassificationPreprocessor, TextSingleColumnRegressionPreprocessor
  from autotrain.preprocessor.vision import ImageClassificationPreprocessor
label_desc: Use image node_id 3 for calling the AutoTrainDataset obj's underlying member method code with example usage: obj._preprocess_data() and returns: train_df,  valid_df